My research mainly focus on deep reinforcement learning (RL) and transfer learning.
We aimed at designing agents that take decisions in an unknown environment, and learn through their own interaction with this environment to maximize a given criterion.
Specifically, I work on model-free actor-critic algorithms with continuous environments (in state and action) trying to make them more data-efficient. The environments considered were often related to control tasks and robotic simulations.

Keywords:

Reinforcement Learning, Neural Networks, Transfer Learning, Developmental Learning, Machine Learning, Actor-Critic

Selected Publications

[1] Matthieu Zimmer and Paul Weng. Exploiting the sign of the advantage function to learn deterministic policies in continuous domains. In International Joint Conferences on Artificial Intelligence, August 2019.
bib | poster | hal pdf | source code | slides ]
[2] Matthieu Zimmer, Yann Boniface, and Alain Dutech. Developmental reinforcement learning through sensorimotor space enlargement. In The 8th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics, September 2018.
bib | video | hal pdf | source code | slides ]
[3] Matthieu Zimmer and Stephane Doncieux. Bootstrapping q-learning for robotics from neuro-evolution results. IEEE Transactions on Cognitive and Developmental Systems, 2017.
bib | video | hal pdf | pdf ]
Contact:

  contact@matthieu-zimmer.net